JSON Field

Milvus allows you to store and index structured data within a single field using the JSON data type. This enables flexible schemas with nested attributes while still allowing efficient filtering via JSON path indexing.

What is a JSON field?

A JSON field is a schema-defined field in Milvus that stores structured key-value data. The values can include strings, numbers, booleans, arrays, or deeply nested objects.

Here’s an example of what a JSON field might look like in a document:

{
  "metadata": {
    "category": "electronics",
    "brand": "BrandA",
    "in_stock": true,
    "price": 99.99,
    "string_price": "99.99",
    "tags": ["clearance", "summer_sale"],
    "supplier": {
      "name": "SupplierX",
      "country": "USA",
      "contact": {
        "email": "support@supplierx.com",
        "phone": "+1-800-555-0199"
      }
    }
  }
}

In this example:

• metadata is the JSON field defined in the schema.

• You can store flat values (e.g. category, in_stock), arrays (tags), and nested objects (supplier).

Define a JSON field in the schema

To use a JSON field, explicitly define it in the collection schema by specifying the DataType as JSON.

The example below creates a collection with its schema containing these fields:

• The primary key (product_id)

• A vector field (mandatory for each collection)

• A metadata field of type JSON, which can store structured data like flat values, arrays, or nested objects

from pymilvus import MilvusClient, DataType

client = MilvusClient(uri="http://localhost:19530")

# Create schema with a JSON field
schema = client.create_schema(auto_id=False, enable_dynamic_field=True)

schema.add_field(field_name="product_id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=5)
schema.add_field(field_name="metadata", datatype=DataType.JSON, nullable=True)  # JSON field that allows null values

client.create_collection(
    collection_name="product_catalog",
    schema=schema
)

import io.milvus.v2.client.*;
import io.milvus.v2.service.collection.request.CreateCollectionReq;
import io.milvus.v2.service.collection.request.AddFieldReq;

ConnectConfig config = ConnectConfig.builder()
        .uri("http://localhost:19530")
        .build();
MilvusClientV2 client = new MilvusClientV2(config);

CreateCollectionReq.CollectionSchema schema = CreateCollectionReq.CollectionSchema.builder()
        .enableDynamicField(true)
        .build();
        
schema.addField(AddFieldReq.builder()
        .fieldName("product_id")
        .dataType(DataType.Int64)
        .isPrimaryKey(Boolean.TRUE)
        .build());
schema.addField(AddFieldReq.builder()
        .fieldName("vector")
        .dataType(DataType.FloatVector)
        .dimension(5)
        .build());
schema.addField(AddFieldReq.builder()
        .fieldName("metadata")
        .dataType(DataType.JSON)
        .isNullable(true)
        .build());
        
CreateCollectionReq requestCreate = CreateCollectionReq.builder()
        .collectionName("product_catalog")
        .collectionSchema(schema)
        .build();
client.createCollection(requestCreate);

import { MilvusClient, DataType } from '@zilliz/milvus2-sdk-node';

const client = new MilvusClient({
  address: 'localhost:19530'
});

// Create collection
await client.createCollection({
collection_name: "product_catalog",
fields: [
  {
    name: "product_id",
    data_type: DataType.Int64,
    is_primary_key: true,
    autoID: false
  },
  {
    name: "vector",
    data_type: DataType.FloatVector,
    dim: 5
  },
  {
    name: "metadata",
    data_type: DataType.JSON,
    nullable: true  // JSON field that allows null values
  }
],
enable_dynamic_field: true
});

import (
    "context"

    "github.com/milvus-io/milvus/client/v2/entity"
    "github.com/milvus-io/milvus/client/v2/milvusclient"
)

ctx, cancel := context.WithCancel(context.Background())
defer cancel()

client, err := milvusclient.New(ctx, &milvusclient.ClientConfig{
    Address: "localhost:19530",
})
if err != nil {
    return err
}

schema := entity.NewSchema().WithDynamicFieldEnabled(true)
schema.WithField(entity.NewField().
    WithName("product_id").pk
    WithDataType(entity.FieldTypeInt64).
    WithIsPrimaryKey(true),
).WithField(entity.NewField().
    WithName("vector").
    WithDataType(entity.FieldTypeFloatVector).
    WithDim(5),
).WithField(entity.NewField().
    WithName("metadata").
    WithDataType(entity.FieldTypeJSON).
    WithNullable(true),
)

err = client.CreateCollection(ctx, milvusclient.NewCreateCollectionOption("product_catalog", schema))
if err != nil {
    return err
}

# restful
export TOKEN="root:Milvus"
export CLUSTER_ENDPOINT="http://localhost:19530"

# 字段定义
export productIdField='{
  "fieldName": "product_id",
  "dataType": "Int64",
  "isPrimary": true,
  "autoID": false
}'

export vectorField='{
  "fieldName": "vector",
  "dataType": "FloatVector",
  "typeParams": {
    "dim": 5
  }
}'

export metadataField='{
  "fieldName": "metadata",
  "dataType": "JSON",
  "isNullable": true
}'

# 构造 schema
export schema="{
  \"autoID\": false,
  \"enableDynamicField\": true,
  \"fields\": [
    $productIdField,
    $vectorField,
    $metadataField
  ]
}"

# 创建集合
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/collections/create" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
--data "{
  \"collectionName\": \"product_catalog\",
  \"schema\": $schema
}"

Insert entities with JSON data

Once the collection is created, insert entities that contain structured JSON objects in the metadata JSON field.

entities = [
    {
        "product_id": 1,
        "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
        "metadata": {
            "category": "electronics",
            "brand": "BrandA",
            "in_stock": True,
            "price": 99.99,
            "string_price": "99.99",
            "tags": ["clearance", "summer_sale"],
            "supplier": {
                "name": "SupplierX",
                "country": "USA",
                "contact": {
                    "email": "support@supplierx.com",
                    "phone": "+1-800-555-0199"
                }
            }
        }
    }
]

client.insert(collection_name="product_catalog", data=entities)

import com.google.gson.Gson;
import com.google.gson.JsonObject;

import io.milvus.v2.service.vector.request.InsertReq;

Gson gson = new Gson();
JsonObject row = new JsonObject();
row.addProperty("product_id", 1);
row.add("vector", gson.toJsonTree(Arrays.asList(0.1, 0.2, 0.3, 0.4, 0.5)));

JsonObject metadata = new JsonObject();
metadata.addProperty("category", "electronics");
metadata.addProperty("brand", "BrandA");
metadata.addProperty("in_stock", true);
metadata.addProperty("price", 99.99);
metadata.addProperty("string_price", "99.99");
metadata.add("tags", gson.toJsonTree(Arrays.asList("clearance", "summer_sale")));

JsonObject supplier = new JsonObject();
supplier.addProperty("name", "SupplierX");
supplier.addProperty("country", "USA");

JsonObject contact = new JsonObject();
contact.addProperty("email", "support@supplierx.com");
contact.addProperty("phone", "+1-800-555-0199");

supplier.add("contact", contact);
metadata.add("supplier", supplier);
row.add("metadata", metadata);

client.insert(InsertReq.builder()
        .collectionName("product_catalog")
        .data(Collections.singletonList(row))
        .build());

const entities = [
    {
        "product_id": 1,
        "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
        "metadata": {
            "category": "electronics",
            "brand": "BrandA",
            "in_stock": True,
            "price": 99.99,
            "string_price": "99.99",
            "tags": ["clearance", "summer_sale"],
            "supplier": {
                "name": "SupplierX",
                "country": "USA",
                "contact": {
                    "email": "support@supplierx.com",
                    "phone": "+1-800-555-0199"
                }
            }
        }
    }
]

await client.insert({
    collection_name: "product_catalog", 
    data: entities
});

_, err = client.Insert(ctx, milvusclient.NewColumnBasedInsertOption("product_catalog").
    WithInt64Column("product_id", []int64{1}).
    WithFloatVectorColumn("vector", 5, [][]float32{
        {0.1, 0.2, 0.3, 0.4, 0.5},
    }).WithColumns(
    column.NewColumnJSONBytes("metadata", [][]byte{
        []byte(`{
            "category": "electronics",
            "brand": "BrandA",
            "in_stock": True,
            "price": 99.99,
            "string_price": "99.99",
            "tags": ["clearance", "summer_sale"],
            "supplier": {
                "name": "SupplierX",
                "country": "USA",
                "contact": {
                    "email": "support@supplierx.com",
                    "phone": "+1-800-555-0199"
                }
            }
        }`),
    }),
))
if err != nil {
    return err
}

# restful
export TOKEN="root:Milvus"
export CLUSTER_ENDPOINT="http://localhost:19530"

export entities='[
  {
    "product_id": 1,
    "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
    "metadata": {
      "category": "electronics",
      "brand": "BrandA",
      "in_stock": true,
      "price": 99.99,
      "string_price": "99.99",
      "tags": ["clearance", "summer_sale"],
      "supplier": {
        "name": "SupplierX",
        "country": "USA",
        "contact": {
          "email": "support@supplierx.com",
          "phone": "+1-800-555-0199"
        }
      }
    }
  }
]'

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/collections/product_catalog/insert" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
--data "{
  \"data\": $entities
}"

Index values inside the JSON fieldCompatible with Milvus 2.5.11+

To accelerate scalar filtering on JSON fields, Milvus supports indexing JSON fields using JSON path indexing. This allows you to filter by keys or nested values inside a JSON object without scanning the entire field.

JSON path indexing syntax

To create a JSON path index, specify:

• JSON path (json_path): The path to the key or nested field within your JSON object that you want to index.

  • Example: metadata["category"]

    This defines where the indexing engine should look inside the JSON structure.

• JSON cast type (json_cast_type): The data type that Milvus should use when interpreting and indexing the value at the specified path.

  • This type must match the actual data type of the field being indexed. If you want to convert the data type to another during indexing, consider using a cast function.

  • For a complete list, see below.

Supported JSON cast types

Cast types are case-insensitive. The following types are supported:

Cast Type	Description	Example JSON Value
bool	Boolean value	true, false
double	Numeric value (integer or float)	42, 99.99, -15.5
varchar	String value	"electronics", "BrandA"
array_bool	Array of booleans	[true, false, true]
array_double	Array of numbers	[1.2, 3.14, 42]
array_varchar	Array of strings	["tag1", "tag2", "tag3"]

Example: Create JSON path indexes

Using the metadata JSON structure from our introduction, here are examples of how to create indexes on different JSON paths:

# Index the category field as a string
index_params = client.prepare_index_params()

index_params.add_index(
    field_name="metadata",
    index_type="AUTOINDEX", # Must be set to AUTOINDEX or INVERTED for JSON path indexing
    index_name="category_index",  # Unique index name
    params={
        "json_path": "metadata[\"category\"]", # Path to the JSON key to be indexed
        "json_cast_type": "varchar" # Data cast type
    }
)

# Index the tags array as string array
index_params.add_index(
    field_name="metadata",
    index_type="AUTOINDEX", # Must be set to AUTOINDEX or INVERTED for JSON path indexing
    index_name="tags_array_index", # Unique index name
    params={
        "json_path": "metadata[\"tags\"]", # Path to the JSON key to be indexed
        "json_cast_type": "array_varchar" # Data cast type
    }
)

import io.milvus.v2.common.IndexParam;

Map<String,Object> extraParams1 = new HashMap<>();
extraParams1.put("json_path", "metadata[\"category\"]");
extraParams1.put("json_cast_type", "varchar");
indexParams.add(IndexParam.builder()
        .fieldName("metadata")
        .indexName("category_index")
        .indexType(IndexParam.IndexType.AUTOINDEX)
        .extraParams(extraParams1)
        .build());

Map<String,Object> extraParams2 = new HashMap<>();
extraParams2.put("json_path", "metadata[\"tags\"]");
extraParams2.put("json_cast_type", "array_varchar");
indexParams.add(IndexParam.builder()
        .fieldName("metadata")
        .indexName("tags_array_index")
        .indexType(IndexParam.IndexType.AUTOINDEX)
        .extraParams(extraParams2)
        .build());

const indexParams = [
  {
    collection_name: "product_catalog",
    field_name: "metadata",
    index_name: "category_index",
    index_type: "AUTOINDEX", // Can also use "INVERTED" for JSON path indexing
    extra_params: {
      json_path: 'metadata["category"]',
      json_cast_type: "varchar",
    },
  },
  {
    collection_name: "product_catalog",
    field_name: "metadata",
    index_name: "tags_array_index",
    index_type: "AUTOINDEX", // Can also use "INVERTED" for JSON path indexing
    extra_params: {
      json_path: 'metadata["tags"]',
      json_cast_type: "array_varchar",
    },
  },
];

import (
    "github.com/milvus-io/milvus/client/v2/index"
)

jsonIndex1 := index.NewJSONPathIndex(index.AUTOINDEX, "varchar", `metadata["category"]`)
    .WithIndexName("category_index")
jsonIndex2 := index.NewJSONPathIndex(index.AUTOINDEX, "array_varchar", `metadata["tags"]`)
    .WithIndexName("tags_array_index")

indexOpt1 := milvusclient.NewCreateIndexOption("product_catalog", "metadata", jsonIndex1)
indexOpt2 := milvusclient.NewCreateIndexOption("product_catalog", "metadata", jsonIndex2)

# restful
export categoryIndex='{
  "fieldName": "metadata",
  "indexName": "category_index",
  "params": {
    "index_type": "AUTOINDEX",
    "json_path": "metadata[\\\"category\\\"]",
    "json_cast_type": "varchar"
  }
}'

export tagsArrayIndex='{
  "fieldName": "metadata",
  "indexName": "tags_array_index",
  "params": {
    "index_type": "AUTOINDEX",
    "json_path": "metadata[\\\"tags\\\"]",
    "json_cast_type": "array_varchar"
  }
}'

Use JSON cast functions for type conversionCompatible with Milvus 2.5.14+

If your JSON field key contains values in an incorrect format (e.g., numbers stored as strings), you can use cast functions to convert values during indexing.

Supported cast functions

Cast functions are case-insensitive. The following types are supported:

Cast Function	Converts From → To	Use Case
"STRING_TO_DOUBLE"	String → Numeric (double)	Convert "99.99" to 99.99

Example: Cast string numbers to double

# Convert string numbers to double for indexing
index_params.add_index(
    field_name="metadata",
    index_type="AUTOINDEX", # Must be set to AUTOINDEX or INVERTED for JSON path indexing
    index_name="string_to_double_index", # Unique index name
    params={
        "json_path": "metadata[\"string_price\"]", # Path to the JSON key to be indexed
        "json_cast_type": "double", # Data cast type
        "json_cast_function": "STRING_TO_DOUBLE" # Cast function; case insensitive
    }
)

Map<String,Object> extraParams3 = new HashMap<>();
extraParams3.put("json_path", "metadata[\"string_price\"]");
extraParams3.put("json_cast_type", "double");
extraParams3.put("json_cast_function", "STRING_TO_DOUBLE");
indexParams.add(IndexParam.builder()
        .fieldName("metadata")
        .indexName("string_to_double_index")
        .indexType(IndexParam.IndexType.AUTOINDEX)
        .extraParams(extraParams3)
        .build());

indexParams.push({
  collection_name: "product_catalog",
  field_name: "metadata",
  index_name: "string_to_double_index",
  index_type: "AUTOINDEX", // Can also use "INVERTED"
  extra_params: {
    json_path: 'metadata["string_price"]',
    json_cast_type: "double",
    json_cast_function: "STRING_TO_DOUBLE", // Case insensitive
  },
});

jsonIndex3 := index.NewJSONPathIndex(index.AUTOINDEX, "double", `metadata["string_price"]`)
                    .WithIndexName("string_to_double_index")

indexOpt3 := milvusclient.NewCreateIndexOption("product_catalog", "metadata", jsonIndex3)

# restful
export stringToDoubleIndex='{
  "fieldName": "metadata",
  "indexName": "string_to_double_index",
  "params": {
    "index_type": "AUTOINDEX",
    "json_path": "metadata[\\\"string_price\\\"]",
    "json_cast_type": "double",
    "json_cast_function": "STRING_TO_DOUBLE"
  }
}'

Apply indexes to the collection

After defining the index parameters, you can apply them to the collection using create_index():

client.create_index(
    collection_name="product_catalog",
    index_params=index_params
)

import io.milvus.v2.service.index.request.CreateIndexReq;

client.createIndex(CreateIndexReq.builder()
        .collectionName("product_catalog")
        .indexParams(indexParams)
        .build());

await client.createIndex(indexParams)

indexTask1, err := client.CreateIndex(ctx, indexOpt1)
if err != nil {
    return err
}
indexTask2, err := client.CreateIndex(ctx, indexOpt2)
if err != nil {
    return err
}
indexTask3, err := client.CreateIndex(ctx, indexOpt3)
if err != nil {
    return err
}

# restful
export indexParams="[
  $categoryIndex,
  $tagsArrayIndex,
  $stringToDoubleIndex
]"
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/indexes/create" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
--data "{
  \"collectionName\": \"product_catalog\",
  \"indexParams\": $indexParams
}"

Filter by JSON field values

After inserting and indexing JSON fields, you can filter on them using standard filter expressions with JSON path syntax.

For example:

filter = 'metadata["category"] == "electronics"'
filter = 'metadata["price"] > 50'
filter = 'json_contains(metadata["tags"], "featured")'

String filter = 'metadata["category"] == "electronics"';
String filter = 'metadata["price"] > 50';
String filter = 'json_contains(metadata["tags"], "featured")';

let filter = 'metadata["category"] == "electronics"'
let filter = 'metadata["price"] > 50'
let filter = 'json_contains(metadata["tags"], "featured")'

filter := 'metadata["category"] == "electronics"'
filter := 'metadata["price"] > 50'
filter := 'json_contains(metadata["tags"], "featured")'

# restful
export filterCategory='metadata["category"] == "electronics"'
export filterPrice='metadata["price"] > 50'
export filterTags='json_contains(metadata["tags"], "featured")'

To use these expressions in a search or query, make sure:

• You have created an index on each vector field.

• The collection is loaded into memory.

For a full list of supported operators and expressions, refer to JSON Operators.

Pull it all together

By now, you’ve learned how to define, insert, and optionally index structured values inside a JSON field.

To complete the workflow in a real-world application, you’ll also need to:

• Create an index on your vector fields (mandatory for each vector field in a collection)

  Refer to Set Index Parameters

• Load the collection

  Refer to Load & Release

• Search or query using JSON path filters

  Refer to Filtered Search and JSON Operators

FAQ

What are the differences between a JSON field and the dynamic field?

• JSON field is schema-defined. You must explicitly declare the field in the schema.

• Dynamic field is a hidden JSON object ($meta) that automatically stores any field not defined in the schema.

Both support nested structures and JSON path indexing, but dynamic fields are more suitable for optional or evolving data structures.

Refer to Dynamic Field for details.

Are there any limitations on the size of a JSON field?

Yes. Each JSON field is limited to 65,536 bytes.

Does a JSON field support setting a default value?

No, JSON fields do not support default values. However, you can set nullable=True when defining the field to allow empty entries.

Refer to Nullable & Default for details.

Are there any naming conventions for JSON field keys?

Yes, to ensure compatibility with queries and indexing:

• Use only letters, numbers, and underscores in JSON keys.

• Avoid using special characters, spaces, or dots (., /, etc.).

• Incompatible keys may cause parsing issues in filter expressions.

How does Milvus handle string values in JSON fields?

Milvus stores string values exactly as they appear in the JSON input—without semantic transformation. Improperly quoted strings may result in errors during parsing.

Examples of valid strings:

"a\"b", "a'b", "a\\b"

Examples of invalid strings:

'a"b', 'a\'b'

What filtering logic does Milvus use for indexed JSON paths?

• Numeric Indexing:

  If an index is created with json_cast_type="double", only numeric filter conditions (e.g., >, <, == 42) will leverage the index. Non-numeric conditions may fall back to a brute-force scan.

• String Indexing:

  If an index uses json_cast_type="varchar", only string filter conditions will benefit from the index; other types may fall back to a brute-force scan.

• Boolean Indexing:

  Boolean indexing behaves similarly to string indexing, with index usage only when the condition strictly matches true or false.

What about numeric precision when indexing JSON fields?

Milvus stores all indexed numeric values as doubles.

If a numeric value exceeds 2^53, it may lose precision. This loss of precision can result in filter queries not matching out-of-range values exactly.

Can I create multiple indexes on the same JSON path with different cast types?

No, each JSON path supports only one index. You must choose a single json_cast_type that matches your data. Creating multiple indexes on the same path with different cast types is not supported.

What if values on a JSON path have inconsistent types?

Inconsistent types across entities can lead to partial indexing. For example, if metadata["price"] is stored as both a number (99.99) and a string ("99.99"), and the index is defined with json_cast_type="double", only the numeric values will be indexed. The string-form entries will be skipped and not appear in filter results.

Can I use filters with a different type than the indexed cast type?

If your filter expression uses a different type than the index’s json_cast_type, the system will not use the index, and may fall back to a slower brute-force scan—if the data allows. For best performance, always align your filter expression with the cast type of the index.